Massively Parallel ECoG Signal Processing using MPSoC for Real-time Brain–Computer Interface Kareemullah H.*, Janakiraman N.**, Kumar P. Nirmal*** *Research Scholar, Department of ECE, Anna University, Chennai, India **Department of ECE, K.L.N. College of Engineering, Madurai, India ***Department of ECE, Anna University, Chennai, India Online published on 23 March, 2017. Abstract Bio-medical signal contains large quantities of data and it should be processed in a short period of time face. The development of modern computer based processing system provides extreme support for bio-medical signal analysis. It is a challenging work to process all the data recorded from an electrode array with high channel counts and bandwidth, such as electroencephalographic (EEG) and electrocorticographic (ECoG) grids. Therefore, a novel parallel processing method was developed for real-time neural signal processing of a brain–computer interface (BCI). The Multiprocessor system-on-chip (MPSoC) system was used to offload processing with the support of multi-core technology, which is capable of running many operations in parallel. The proposed design supports reconfigurable embedded computing techniques which can be portable, economical and cost saving system. The BCI system traces many channels of data. It may be processed and translated into a control signal, such as the movement of a cursor towards the indicated direction. This signal processing chain has three steps which are matrix–matrix multiplication technique, power spectral density calculation on every channel and classification of appropriate features for control. In this study, the BCI signal processing chain is implemented on the proposed embedded computing based SoC system, and compared with the current BCI system in terms of speed and accuracy. Significant performance gains were obtained in this study. The current BCI system processed 1000 channels of 250 ms in 1486 ms with 68% accuracy, while the proposed method took only 48 ms, an improvement of nearly 31 times. Top Keywords Electroencephalography, Electrocorticography, Brain-Computer Interface, MPSoC, Reconfigurable systems, Embedded computing. Top |